Differential Decline in Terrestrial Water Storage Across Major Permafrost‐Dominated Arctic River Basins During the Rapid Warming Period From 1981 to 2020
Bibliographic record
Abstract
Abstract Terrestrial water storage (TWS) in northern high‐latitude regions is strongly influenced by climate warming and the resulting permafrost thaw. However, it is not yet fully understood how different permafrost types constrain TWS changes during the rapid warming period. In this study, we focused on the six major Arctic river basins (Ob', Yenisei, Lena, Kolyma, Yukon, and Mackenzie), which are characterized by widespread permafrost, and employed three TWS products derived from remote sensing observations, land surface models, and reanalysis data sets to quantify changes in TWS anomalies during the rapid warming period (1981–2020). Statistical analyses revealed differential TWS declines across all permafrost types, with the most significant decline observed in the discontinuous permafrost regions at −3.05 mm/year, compared to in the continuous permafrost regions (−0.78 mm/year) and in the sporadic permafrost regions (−2.45 mm/year). Correlation analyses further indicated a pronounced negative relationship between permafrost active layer thickness (ALT) and TWS, especially in discontinuous permafrost regions, where a 1‐cm increase in ALT corresponded to a TWS decrease of up to 4.4‐mm. These findings highlight the significant impact of permafrost thawing accelerated by climate warming on TWS changes in permafrost‐dominated Arctic regions, with important implications for regional hydrology, carbon feedback, and ecosystem stability in the pan‐Arctic. Our results underscore the necessity of incorporating permafrost‐specific processes into hydrological models and climate assessments, thereby enhancing projections of water resource a4vailability and environmental changes in northern high‐latitude regions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".